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Anomaly Classification On Goodsad

Metrics

AUPR

AUROC

Results

Performance results of various models on this benchmark

Model Name	AUPR	AUROC	Paper Title	Repository
PatchCore-100%	86.1	85.5	Towards Total Recall in Industrial Anomaly Detection	-
PatchCore-1%	83.3	81.4	Towards Total Recall in Industrial Anomaly Detection	-
SimpleNet	78.7	75.3	SimpleNet: A Simple Network for Image Anomaly Detection and Localization	-
RD4AD	68.2	66.5	Anomaly Detection via Reverse Distillation from One-Class Embedding	-
DRAEM	71	65.9	DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection	-
SPADE	68.7	64.1	Sub-Image Anomaly Detection with Deep Pyramid Correspondences	-
NSA	71.8	67.3	Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization	-
CFLOW-AD	75.3	71.2	CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows	-
MiniMaxAD-fr	-	86.1	MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection	-
CutPaste	62.8	60.2	CutPaste: Self-Supervised Learning for Anomaly Detection and Localization	-
f-AnoGAN	66.6	62.8	f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks

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Anomaly Classification On Goodsad | SOTA | HyperAI